Quality Evaluation in Guavas using Deep Learning Architectures: An Experimental Review

Choudhury, Tanupriya; Mahdi, Hussain Falih; Agarwal, Archit; Chakraborty, Arnav; Arunachalaeshwaran, V. R.; Sarkar, T.; Tomar, Ravi

doi:10.1109/hora55278.2022.9799824

Cited by 10 publications

(2 citation statements)

References 16 publications

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“…Our model has achieved 78% and 76% accuracy in the training and the validation set. In addition, the deep learning [27][28][29] methodologies strategically developed and the methodology accuracy also helped to justify the model architecture.…”

Section: Discussionmentioning

confidence: 99%

An Efficient Deep Neural Framework for Nucleus Semantic Segmentation with Enhanced U-Net

Sawhney¹,

Sharma²,

Vashishtha³

et al. 2022

RIA

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With the advancement of medicine, there is a terrific requirement to process an increasing number of medical imaging where image segmentation comes into play. Single cell division is generally among the first as well as most essential tasks in image-based cell analysis. The identification of the nuclei allows pathologists to determine each cell in the sample, and by measuring how cells respond to various treatments, they can comprehend the fundamental biological processes in work. In this paper we gathered a deep learning network which detects and parts the cell microscopy image. Highly advanced performance is achieved in image segmentation tasks through deep learning-based techniques. These procedures are complex and need the support of compelling computational resources. This paper emphasizes the basic principles of the methods used to segment an image. We have implemented U-Net for the semantic segmentation of nucleus. A successful implementation will aid researchers immensely in their fight to find pharmaceutical solutions to medical crises while saving both valuable research time and funding.

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Section: Discussionmentioning

confidence: 99%

An Efficient Deep Neural Framework for Nucleus Semantic Segmentation with Enhanced U-Net

Sawhney¹,

Sharma²,

Vashishtha³

et al. 2022

RIA

Self Cite

View full text Add to dashboard Cite

show abstract

“…The importance of this research transcends theoretical investigation, extending to substantial practical implications. Through the analytical prediction of future trends in agricultural product trade, it has enabled policymakers to sculpt strategies with heightened precision, consequently bolstering the global competitiveness of agricultural trade (Choudhury et al, 2022;Deng and Gibson, 2019;Tarfi et al, 2023).…”

Section: Quantile Factor Modelmentioning

confidence: 99%

Enhancing agricultural product trade efficiency through machine learning predictions and multi-objective optimization of financial strategies

2024

Qual. Assur. Saf. Crops Foods

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In the context of global economic development and food safety, agricultural trade plays a vital role in linking agricultural production with market demand, and the efficient formulation of agricultural financial strategies is crucial for enhancing trade efficiency. This study employs advanced machine learning technologies to predict and optimize the efficiency of agricultural trade while seeking to balance the interests of various stakeholders in agricultural finance. By integrating quantile factor models, long short-term memory (LSTM) networks, and attention mechanisms, this paper conducts an in-depth analysis and precise prediction of the key factors affecting trade efficiency. This approach effectively addresses the nonlinearity and long-term dependency issues in time-series data, utilizing attention mechanisms to highlight critical information and improve prediction accuracy. Furthermore, this research establishes a multi-objective optimization model to balance the interests of agricultural finance participants, providing a new quantitative tool for formulating agricultural financial strategies aimed at optimizing decisions to enhance economic and social value. This paper offers new perspectives, methods, and empirical support for improving the efficiency of agricultural trade and the formulation of agricultural financial strategies.

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CNN-Based Freshness Grading of Mourala Fish (Amblypharyngodon Mola)

Choudhury

Aryan

Mahdi

et al. 2023

Lecture Notes in Networks and Systems

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Quality Evaluation in Guavas using Deep Learning Architectures: An Experimental Review

Cited by 10 publications

References 16 publications

An Efficient Deep Neural Framework for Nucleus Semantic Segmentation with Enhanced U-Net

An Efficient Deep Neural Framework for Nucleus Semantic Segmentation with Enhanced U-Net

Enhancing agricultural product trade efficiency through machine learning predictions and multi-objective optimization of financial strategies

CNN-Based Freshness Grading of Mourala Fish (Amblypharyngodon Mola)

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